Personalized Action Suggestion System Using User Segmentation
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Solution Overview
Problem
Existing systems for suggesting actions to users either rely on aggregated feedback that can be distorted by erroneous inputs or fail to personalize actions based on individual user specifics, leading to unsuitable action lists.
Innovation Solution
A system that uses a server and automaton to analyze user images and context data, identify the user, and calculate personalized action lists based on previous selections in similar contexts, incorporating user profiles and context information to provide tailored recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If aggregated feedback is used to suggest actions, then the system can operate without complex user identification, but the feedback can be distorted by erroneous inputs and cannot be personalized
Solution Approach 1:
The patent segments the aggregated feedback into individual user feedback records, each associated with a specific user identifier. This allows the system to process feedback from multiple users separately, preventing distortion from erroneous inputs and enabling personalized action suggestions for each user based on their individual feedback patterns.
Solution Approach 2:
The system implements a feedback mechanism where user responses are collected, processed, and used to generate personalized action suggestions. The feedback loop allows the system to learn from user interactions and continuously improve the relevance and accuracy of suggested actions for each individual user.
2Adaptability or versatility
If user identification is implemented to personalize action lists, then the system can provide tailored recommendations, but the system complexity increases
Solution Approach 1:
The patent segments the user base into individually identifiable entities, creating separate profiles for each user. This segmentation enables the system to store and retrieve user-specific information, allowing personalized action lists while managing complexity through structured data organization and modular processing components.
Solution Approach 2:
The system applies local quality by providing different action lists tailored to each user's specific preferences, history, and context. Instead of a uniform approach, the system customizes the content and structure of action suggestions for each individual user, enhancing adaptability while using efficient algorithms to manage the increased complexity.
3Productivity
If context data analysis is used to generate action lists, then the system can provide relevant suggestions, but the lists are not personalized to individual user specifics
Solution Approach 1:
The patent merges two previously separate approaches: context data analysis and user identification. By combining contextual information about the situation with individual user profiles and preferences, the system generates action lists that are both relevant to the current context and personalized to each user's specific characteristics, history, and preferences.
Solution Approach 2:
The system dynamically changes parameters by adjusting the weight and importance of different factors (context data versus user preferences) when generating action lists. This allows the system to optimize the balance between providing contextually relevant suggestions and personalizing them according to individual user specifics, enhancing both productivity and adaptability.
Data Source
AI summary
A system for suggesting a list of actions to a user includes a server and an automaton connected to the server. The system makes it possible to calculate a list of actions that is suited to a user while taking into account the identity of the user, the profile of the user, and the first context data. The profile of the user includes: a unique identifier associated with the identity of the user, a history of the selections already made by the user on the automaton, and the second context data and the datum representing the return of the user that are related to each selection already made. Thus it is possible to suggest, to a user, a list of actions wherein the list of actions is established on the basis of analysis of the selections previously made in a similar context by a pre-identified user.